cb043911fe
properties to help distinguish between metrics that should and shouldn't be passed sample_weight argument. Note these properties are set to None before Model.fit is called, since metrics are potentially broadcast to match the structure of data seen in Model.fit. PiperOrigin-RevId: 339892649 Change-Id: I0abffae08efde2b8adc58014ef205d318d66a9ab
740 lines
29 KiB
Python
740 lines
29 KiB
Python
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for compile utitilies."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.python.distribute import one_device_strategy
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.keras import backend as K
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from tensorflow.python.keras import keras_parameterized
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from tensorflow.python.keras import losses as losses_mod
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from tensorflow.python.keras import metrics as metrics_mod
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from tensorflow.python.keras.engine import compile_utils
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.platform import test
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class LossesContainerTest(keras_parameterized.TestCase):
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def test_single_loss(self):
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loss_container = compile_utils.LossesContainer('mse')
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y_t, y_p = array_ops.ones((10, 5)), array_ops.zeros((10, 5))
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total_loss = loss_container(y_t, y_p)
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self.assertTrue(loss_container._built)
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self.assertLen(loss_container._losses, 1)
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self.assertEqual(total_loss.numpy(), 1.)
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self.assertLen(loss_container.metrics, 1)
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loss_metric = loss_container.metrics[0]
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self.assertEqual(loss_metric.name, 'loss')
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self.assertEqual(loss_metric.result().numpy(), 1.)
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def test_loss_list(self):
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loss_container = compile_utils.LossesContainer(['mse', 'mae'], [1, 0.5])
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y_t = [array_ops.ones((10, 1)), array_ops.zeros((10, 1))]
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y_p = [array_ops.ones((10, 1)), array_ops.ones((10, 1))]
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sw = ops.convert_to_tensor_v2_with_dispatch([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
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total_loss = loss_container(y_t, y_p, sample_weight=sw)
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self.assertEqual(loss_container._output_names, ['output_1', 'output_2'])
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self.assertLen(loss_container._losses, 2)
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self.assertEqual(total_loss.numpy(), 0.25)
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loss_metric = loss_container.metrics[0]
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self.assertEqual(loss_metric.name, 'loss')
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self.assertEqual(loss_metric.result().numpy(), 0.25)
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output_1_metric = loss_container.metrics[1]
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self.assertEqual(output_1_metric.name, 'output_1_loss')
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self.assertEqual(output_1_metric.result().numpy(), 0)
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output_2_metric = loss_container.metrics[2]
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self.assertEqual(output_2_metric.name, 'output_2_loss')
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self.assertEqual(output_2_metric.result().numpy(), 0.5)
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def test_loss_dict(self):
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loss_container = compile_utils.LossesContainer(
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{
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'out1': 'mse',
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'out2': 'mae'
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}, {
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'out1': 1,
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'out2': 0.5
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})
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y_t = {'out1': array_ops.ones((10, 1)), 'out2': array_ops.zeros((10, 1))}
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y_p = {'out1': array_ops.ones((10, 1)), 'out2': array_ops.ones((10, 1))}
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sw = ops.convert_to_tensor_v2_with_dispatch([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
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total_loss = loss_container(y_t, y_p, sample_weight=sw)
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self.assertLen(loss_container._losses, 2)
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self.assertEqual(total_loss.numpy(), 0.25)
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self.assertLen(loss_container.metrics, 3)
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loss_metric = loss_container.metrics[0]
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self.assertEqual(loss_metric.name, 'loss')
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self.assertEqual(loss_metric.result().numpy(), 0.25)
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out1_metric = loss_container.metrics[1]
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self.assertEqual(out1_metric.name, 'out1_loss')
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self.assertEqual(out1_metric.result().numpy(), 0)
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out2_metric = loss_container.metrics[2]
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self.assertEqual(out2_metric.name, 'out2_loss')
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self.assertEqual(out2_metric.result().numpy(), 0.5)
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def test_loss_partial_dict_with_output_names(self):
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loss_container = compile_utils.LossesContainer(
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{'out2': 'mae'}, {'out2': 1.}, output_names=['out1', 'out2'])
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y_t = [array_ops.ones((10, 1)), array_ops.zeros((10, 1))]
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y_p = [array_ops.ones((10, 1)), array_ops.ones((10, 1))]
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sw = ops.convert_to_tensor_v2_with_dispatch([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
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total_loss = loss_container(y_t, y_p, sample_weight=sw)
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self.assertEqual(total_loss.numpy(), 0.5)
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self.assertLen(loss_container.metrics, 2)
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loss_metric = loss_container.metrics[0]
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self.assertEqual(loss_metric.name, 'loss')
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self.assertEqual(loss_metric.result().numpy(), 0.5)
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out2_metric = loss_container.metrics[1]
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self.assertEqual(out2_metric.name, 'out2_loss')
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self.assertEqual(out2_metric.result().numpy(), 0.5)
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def test_loss_dict_with_nones(self):
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loss_container = compile_utils.LossesContainer({
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'out1': None,
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'out2': 'mae'
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})
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y_t = {'out1': array_ops.ones((10, 1)), 'out2': array_ops.zeros((10, 1))}
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y_p = {'out1': array_ops.ones((10, 1)), 'out2': array_ops.ones((10, 1))}
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sw = ops.convert_to_tensor_v2_with_dispatch([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
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total_loss = loss_container(y_t, y_p, sample_weight=sw)
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self.assertEqual(total_loss.numpy(), 0.5)
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self.assertLen(loss_container.metrics, 2)
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loss_metric = loss_container.metrics[0]
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self.assertEqual(loss_metric.name, 'loss')
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self.assertEqual(loss_metric.result().numpy(), 0.5)
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out2_metric = loss_container.metrics[1]
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self.assertEqual(out2_metric.name, 'out2_loss')
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self.assertEqual(out2_metric.result().numpy(), 0.5)
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def test_nested_structure(self):
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loss_container = compile_utils.LossesContainer(
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{
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'b': ['mse', None],
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'a': 'mae'
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}, loss_weights={
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'b': [0.5, 0],
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'a': 1
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})
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y_t = {
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'b': [array_ops.ones((10, 1)),
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array_ops.zeros((10, 1))],
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'a': array_ops.zeros((10, 1))
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}
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y_p = {
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'b': [array_ops.zeros((10, 1)),
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array_ops.zeros((10, 1))],
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'a': array_ops.ones((10, 1))
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}
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sw = ops.convert_to_tensor_v2_with_dispatch([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
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total_loss = loss_container(y_t, y_p, sample_weight=sw)
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self.assertEqual(total_loss.numpy(), 0.75)
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self.assertLen(loss_container.metrics, 3)
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loss_metric = loss_container.metrics[0]
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self.assertEqual(loss_metric.name, 'loss')
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self.assertEqual(loss_metric.result().numpy(), 0.75)
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a_metric = loss_container.metrics[1]
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self.assertEqual(a_metric.name, 'a_loss')
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self.assertEqual(a_metric.result().numpy(), 0.5)
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b_1_metric = loss_container.metrics[2]
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self.assertEqual(b_1_metric.name, 'b_1_loss')
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self.assertEqual(b_1_metric.result().numpy(), 0.5)
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def test_broadcast_single_loss(self):
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loss_container = compile_utils.LossesContainer('mse')
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y_t = [array_ops.ones((10, 1)), array_ops.zeros((10, 1))]
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y_p = [array_ops.ones((10, 1)), array_ops.ones((10, 1))]
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sw = ops.convert_to_tensor_v2_with_dispatch([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
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total_loss = loss_container(y_t, y_p, sample_weight=sw)
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self.assertEqual(total_loss.numpy(), 0.5)
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self.assertLen(loss_container.metrics, 3)
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loss_metric = loss_container.metrics[0]
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self.assertEqual(loss_metric.name, 'loss')
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self.assertEqual(loss_metric.result().numpy(), 0.5)
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output_1_metric = loss_container.metrics[1]
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self.assertEqual(output_1_metric.name, 'output_1_loss')
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self.assertEqual(output_1_metric.result().numpy(), 0.)
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output_2_metric = loss_container.metrics[2]
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self.assertEqual(output_2_metric.name, 'output_2_loss')
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self.assertEqual(output_2_metric.result().numpy(), 0.5)
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def test_missing_label_with_no_loss(self):
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# It's ok to exclude a label if that label has no
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# losses or metrics associated with it.
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loss_container = compile_utils.LossesContainer({
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'output1': 'mse',
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'output3': 'mae'
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})
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y_p = {
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'output1': ops.convert_to_tensor_v2_with_dispatch([[0], [1], [2]]),
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'output2': ops.convert_to_tensor_v2_with_dispatch([[3], [4], [5]]),
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'output3': ops.convert_to_tensor_v2_with_dispatch([[6], [7], [8]])
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}
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y_t = {
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'output1': ops.convert_to_tensor_v2_with_dispatch([[1], [2], [3]]),
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'output3': ops.convert_to_tensor_v2_with_dispatch([[4], [5], [6]])
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}
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total_loss = loss_container(y_t, y_p)
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self.assertEqual(total_loss.numpy(), 3.)
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self.assertLen(loss_container.metrics, 3)
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loss_metric = loss_container.metrics[0]
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self.assertEqual(loss_metric.name, 'loss')
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self.assertEqual(loss_metric.result().numpy(), 3.)
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output_1_metric = loss_container.metrics[1]
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self.assertEqual(output_1_metric.name, 'output1_loss')
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self.assertEqual(output_1_metric.result().numpy(), 1.)
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output_3_metric = loss_container.metrics[2]
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self.assertEqual(output_3_metric.name, 'output3_loss')
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self.assertEqual(output_3_metric.result().numpy(), 2.)
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def test_mismatched_dtypes(self):
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y_t = constant_op.constant([1, 9, 2, -5], shape=(2, 2))
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y_p = constant_op.constant([4, 8, 12, 8],
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shape=(2, 2),
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dtype=dtypes.float32)
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def my_mae(labels, preds):
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self.assertEqual(labels.dtype, dtypes.int32)
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self.assertEqual(preds.dtype, dtypes.float32)
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labels = math_ops.cast(labels, preds.dtype)
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return K.mean(math_ops.abs(preds - labels), axis=-1)
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loss_container = compile_utils.LossesContainer(my_mae)
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total_loss = loss_container(y_t, y_p)
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self.assertEqual(total_loss.dtype, dtypes.float32)
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def test_integer_dtypes(self):
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y_t = constant_op.constant([1, 9, 2, -5], shape=(2, 2))
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y_p = constant_op.constant([4, 8, 12, 8], shape=(2, 2), dtype=dtypes.int64)
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def my_mae(labels, preds):
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self.assertEqual(labels.dtype, dtypes.int64)
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self.assertEqual(preds.dtype, dtypes.int64)
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return K.mean(math_ops.abs(preds - labels), axis=-1)
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loss_container = compile_utils.LossesContainer(my_mae)
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total_loss = loss_container(y_t, y_p)
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self.assertEqual(total_loss.dtype, dtypes.int64)
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def test_float_dtypes(self):
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y_t = constant_op.constant([1, 9, 2, -5],
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shape=(2, 2),
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dtype=dtypes.float32)
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y_p = constant_op.constant([4, 8, 12, 8],
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shape=(2, 2),
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dtype=dtypes.float64)
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def my_mae(labels, preds):
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self.assertEqual(labels.dtype, dtypes.float64)
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self.assertEqual(preds.dtype, dtypes.float64)
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return K.mean(math_ops.abs(preds - labels), axis=-1)
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loss_container = compile_utils.LossesContainer(my_mae)
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total_loss = loss_container(y_t, y_p)
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self.assertEqual(total_loss.dtype, dtypes.float64)
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def test_loss_masking(self):
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loss_container = compile_utils.LossesContainer('mae')
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y_p = constant_op.constant([[[1], [1]], [[0], [0]]], dtype=dtypes.float32)
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y_t = constant_op.constant([[[1], [1]], [[1], [1]]], dtype=dtypes.float32)
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y_p._keras_mask = constant_op.constant([[1, 0], [1, 0]],
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dtype=dtypes.float32)
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total_loss = loss_container(y_t, y_p)
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self.assertAlmostEqual(total_loss.numpy(), .25) # sum over batch size
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self.assertLen(loss_container.metrics, 1)
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loss_metric = loss_container.metrics[0]
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self.assertEqual(loss_metric.name, 'loss')
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self.assertAlmostEqual(loss_metric.result().numpy(), .25)
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def test_loss_sample_weight(self):
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loss_container = compile_utils.LossesContainer('mae')
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y_p = constant_op.constant([[[1], [1]], [[0], [0]]], dtype=dtypes.float32)
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y_t = constant_op.constant([[[1], [1]], [[1], [1]]], dtype=dtypes.float32)
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sw = constant_op.constant([[.2, .3], [.5, 0]], dtype=dtypes.float32)
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total_loss = loss_container(y_t, y_p, sample_weight=sw)
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# (0 * .2 + 0 * .3 + 1 * .5 + 1 * 0) / 4
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self.assertAlmostEqual(total_loss.numpy(), .125)
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self.assertLen(loss_container.metrics, 1)
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loss_metric = loss_container.metrics[0]
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self.assertEqual(loss_metric.name, 'loss')
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self.assertAlmostEqual(loss_metric.result().numpy(), .125)
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def test_loss_masking_sample_weight(self):
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loss_container = compile_utils.LossesContainer('mae')
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y_p = constant_op.constant([[[1], [1]], [[0], [0]]], dtype=dtypes.float32)
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y_t = constant_op.constant([[[1], [1]], [[1], [1]]], dtype=dtypes.float32)
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sw = constant_op.constant([[.2, .3], [.5, 0]], dtype=dtypes.float32)
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y_p._keras_mask = constant_op.constant([[1, 0], [1, 0]],
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dtype=dtypes.float32)
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total_loss = loss_container(y_t, y_p, sample_weight=sw)
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# (0 * .2 + 1 * .5) / 4
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self.assertAlmostEqual(total_loss.numpy(), .125) # sum over batch size
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self.assertLen(loss_container.metrics, 1)
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loss_metric = loss_container.metrics[0]
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self.assertEqual(loss_metric.name, 'loss')
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self.assertAlmostEqual(loss_metric.result().numpy(), .125)
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def test_custom_loss_callables(self):
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def custom_loss_fn(y_true, y_pred):
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return math_ops.reduce_sum(y_true - y_pred)
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class CustomLossClass(object):
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def __call__(self, y_true, y_pred):
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return math_ops.reduce_sum(y_true - y_pred)
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loss_container = compile_utils.LossesContainer(
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[custom_loss_fn, CustomLossClass()])
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y_t, y_p = array_ops.ones((10, 5)), array_ops.zeros((10, 5))
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loss_container(y_t, y_p)
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self.assertEqual(loss_container._losses[0].name, 'custom_loss_fn')
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self.assertEqual(loss_container._losses[1].name, 'custom_loss_class')
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class MetricsContainerTest(keras_parameterized.TestCase):
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def test_single_metric(self):
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metric_container = compile_utils.MetricsContainer('mse')
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y_t, y_p = array_ops.ones((10, 5)), array_ops.zeros((10, 5))
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metric_container.update_state(y_t, y_p)
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self.assertLen(metric_container.metrics, 1)
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metric = metric_container.metrics[0]
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self.assertEqual(metric.name, 'mse')
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self.assertEqual(metric.result().numpy(), 1.)
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def test_list_of_metrics_one_output(self):
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metric_container = compile_utils.MetricsContainer(['mse', 'mae'])
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y_t, y_p = 2 * array_ops.ones((10, 5)), array_ops.zeros((10, 5))
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metric_container.update_state(y_t, y_p)
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self.assertLen(metric_container.metrics, 2)
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mse_metric = metric_container.metrics[0]
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self.assertEqual(mse_metric.name, 'mse')
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self.assertEqual(mse_metric.result().numpy(), 4.)
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mae_metric = metric_container.metrics[1]
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self.assertEqual(mae_metric.name, 'mae')
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self.assertEqual(mae_metric.result().numpy(), 2.)
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def test_list_of_metrics_list_of_outputs(self):
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metric_container = compile_utils.MetricsContainer(
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metrics=['mse', 'mae'], # Should broadcast to both outputs.
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weighted_metrics=['accuracy']) # Should broadcast to both outputs.
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y_t = [array_ops.ones((10, 1)), array_ops.zeros((10, 1))]
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y_p = [array_ops.ones((10, 1)), 2 * array_ops.ones((10, 1))]
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sw = ops.convert_to_tensor_v2_with_dispatch([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
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metric_container.update_state(y_t, y_p, sample_weight=sw)
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self.assertLen(metric_container.metrics, 6)
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mse_metric = metric_container.metrics[0]
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self.assertEqual(mse_metric.name, 'output_1_mse')
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self.assertEqual(mse_metric.result().numpy(), 0.)
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mse_metric = metric_container.metrics[1]
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self.assertEqual(mse_metric.name, 'output_1_mae')
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self.assertEqual(mse_metric.result().numpy(), 0.)
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acc_metric_1 = metric_container.metrics[2]
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self.assertEqual(acc_metric_1.name, 'output_1_accuracy')
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self.assertEqual(acc_metric_1.result().numpy(), 1.)
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self.assertEqual(acc_metric_1._fn, metrics_mod.binary_accuracy)
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|
mae_metric = metric_container.metrics[3]
|
|
self.assertEqual(mae_metric.name, 'output_2_mse')
|
|
self.assertEqual(mae_metric.result().numpy(), 4.)
|
|
|
|
mae_metric = metric_container.metrics[4]
|
|
self.assertEqual(mae_metric.name, 'output_2_mae')
|
|
self.assertEqual(mae_metric.result().numpy(), 2.)
|
|
|
|
acc_metric_2 = metric_container.metrics[5]
|
|
self.assertEqual(acc_metric_2.name, 'output_2_accuracy')
|
|
self.assertEqual(acc_metric_2.result().numpy(), 0.)
|
|
self.assertEqual(acc_metric_2._fn, metrics_mod.binary_accuracy)
|
|
|
|
weighted_metrics = metric_container.weighted_metrics
|
|
self.assertLen(weighted_metrics, 2)
|
|
self.assertEqual(weighted_metrics[0].name, 'output_1_accuracy')
|
|
self.assertEqual(weighted_metrics[1].name, 'output_2_accuracy')
|
|
|
|
unweighted_metrics = metric_container.unweighted_metrics
|
|
self.assertLen(unweighted_metrics, 4)
|
|
self.assertEqual(unweighted_metrics[0].name, 'output_1_mse')
|
|
self.assertEqual(unweighted_metrics[1].name, 'output_1_mae')
|
|
self.assertEqual(unweighted_metrics[2].name, 'output_2_mse')
|
|
self.assertEqual(unweighted_metrics[3].name, 'output_2_mae')
|
|
|
|
def test_metric_dict(self):
|
|
metric_container = compile_utils.MetricsContainer(
|
|
metrics={
|
|
'out1': 'mse',
|
|
'out2': 'mae'
|
|
},
|
|
weighted_metrics={
|
|
'out1': 'mse',
|
|
'out2': 'mae'
|
|
})
|
|
|
|
y_t = {'out1': array_ops.ones((10, 1)), 'out2': array_ops.zeros((10, 1))}
|
|
y_p = {'out1': array_ops.ones((10, 1)), 'out2': 2 * array_ops.ones((10, 1))}
|
|
sw = ops.convert_to_tensor_v2_with_dispatch([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
|
|
metric_container.update_state(y_t, y_p, sample_weight=sw)
|
|
|
|
mse_metric = metric_container.metrics[0]
|
|
self.assertEqual(mse_metric.name, 'out1_mse')
|
|
self.assertEqual(mse_metric.result().numpy(), 0.)
|
|
|
|
weighted_mse_metric = metric_container.metrics[1]
|
|
self.assertEqual(weighted_mse_metric.name, 'out1_weighted_mse')
|
|
self.assertEqual(weighted_mse_metric.result().numpy(), 0.)
|
|
|
|
mae_metric = metric_container.metrics[2]
|
|
self.assertEqual(mae_metric.name, 'out2_mae')
|
|
self.assertEqual(mae_metric.result().numpy(), 2.)
|
|
|
|
weighted_mae_metric = metric_container.metrics[3]
|
|
self.assertEqual(weighted_mae_metric.name, 'out2_weighted_mae')
|
|
self.assertEqual(weighted_mae_metric.result().numpy(), 2.)
|
|
|
|
def test_metric_partial_dict_with_output_names(self):
|
|
metric_container = compile_utils.MetricsContainer(
|
|
{'out2': 'mae'}, output_names=['out1', 'out2'])
|
|
|
|
y_t = [array_ops.ones((10, 1)), array_ops.zeros((10, 1))]
|
|
y_p = [array_ops.ones((10, 1)), array_ops.ones((10, 1))]
|
|
sw = ops.convert_to_tensor_v2_with_dispatch([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
|
|
|
|
metric_container.update_state(y_t, y_p, sample_weight=sw)
|
|
self.assertLen(metric_container.metrics, 1)
|
|
|
|
mae_metric = metric_container.metrics[0]
|
|
self.assertEqual(mae_metric.name, 'out2_mae')
|
|
self.assertEqual(mae_metric.result().numpy(), 1.)
|
|
|
|
def test_metric_partial_dict_with_nones(self):
|
|
metric_container = compile_utils.MetricsContainer({
|
|
'out1': None,
|
|
'out2': 'mae'
|
|
})
|
|
|
|
y_t = {'out1': array_ops.ones((10, 1)), 'out2': array_ops.zeros((10, 1))}
|
|
y_p = {'out1': array_ops.ones((10, 1)), 'out2': array_ops.ones((10, 1))}
|
|
sw = ops.convert_to_tensor_v2_with_dispatch([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
|
|
|
|
metric_container.update_state(y_t, y_p, sample_weight=sw)
|
|
self.assertLen(metric_container.metrics, 1)
|
|
|
|
mae_metric = metric_container.metrics[0]
|
|
self.assertEqual(mae_metric.name, 'out2_mae')
|
|
self.assertEqual(mae_metric.result().numpy(), 1.)
|
|
|
|
def test_nested_structure(self):
|
|
metric_container = compile_utils.MetricsContainer(
|
|
metrics={
|
|
'b': ['mse', None],
|
|
'a': 'mae'
|
|
},
|
|
weighted_metrics={
|
|
'b': [None, None],
|
|
'a': 'mse'
|
|
})
|
|
|
|
y_t = {
|
|
'b': [2 * array_ops.ones((10, 1)),
|
|
array_ops.zeros((10, 1))],
|
|
'a': array_ops.zeros((10, 1))
|
|
}
|
|
y_p = {
|
|
'b': [array_ops.zeros((10, 1)),
|
|
array_ops.zeros((10, 1))],
|
|
'a': array_ops.ones((10, 1))
|
|
}
|
|
sw = ops.convert_to_tensor_v2_with_dispatch([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
|
|
|
|
metric_container.update_state(y_t, y_p, sample_weight=sw)
|
|
self.assertLen(metric_container.metrics, 3)
|
|
|
|
a_mae_metric = metric_container.metrics[0]
|
|
self.assertEqual(a_mae_metric.name, 'a_mae')
|
|
self.assertEqual(a_mae_metric.result().numpy(), 1.)
|
|
|
|
weighted_a_mae_metric = metric_container.metrics[1]
|
|
self.assertEqual(weighted_a_mae_metric.name, 'a_mse')
|
|
self.assertEqual(weighted_a_mae_metric.result().numpy(), 1.)
|
|
|
|
b_1_mse_metric = metric_container.metrics[2]
|
|
self.assertEqual(b_1_mse_metric.name, 'b_1_mse')
|
|
self.assertEqual(b_1_mse_metric.result().numpy(), 4.)
|
|
|
|
def test_crossentropy(self):
|
|
metric_container = compile_utils.MetricsContainer('crossentropy')
|
|
y_t, y_p = array_ops.ones((10, 1)), array_ops.ones((10, 1))
|
|
metric_container.update_state(y_t, y_p)
|
|
self.assertEqual(metric_container.metrics[0]._fn,
|
|
metrics_mod.binary_crossentropy)
|
|
|
|
metric_container = compile_utils.MetricsContainer('crossentropy')
|
|
y_t, y_p = array_ops.ones((10, 1)), array_ops.ones((10, 20))
|
|
self.assertEqual(y_p.shape.as_list()[-1], 20)
|
|
metric_container.update_state(y_t, y_p)
|
|
self.assertEqual(metric_container.metrics[0]._fn,
|
|
metrics_mod.sparse_categorical_crossentropy)
|
|
|
|
metric_container = compile_utils.MetricsContainer('crossentropy')
|
|
y_t, y_p = array_ops.ones((10, 20)), array_ops.ones((10, 20))
|
|
metric_container.update_state(y_t, y_p)
|
|
self.assertEqual(metric_container.metrics[0]._fn,
|
|
metrics_mod.categorical_crossentropy)
|
|
|
|
def test_accuracy(self):
|
|
metric_container = compile_utils.MetricsContainer('accuracy')
|
|
y_t, y_p = array_ops.ones((10, 1)), array_ops.ones((10, 1))
|
|
metric_container.update_state(y_t, y_p)
|
|
self.assertEqual(metric_container.metrics[0]._fn,
|
|
metrics_mod.binary_accuracy)
|
|
|
|
metric_container = compile_utils.MetricsContainer('accuracy')
|
|
y_t, y_p = array_ops.ones((10, 1)), array_ops.ones((10, 20))
|
|
self.assertEqual(y_p.shape.as_list()[-1], 20)
|
|
metric_container.update_state(y_t, y_p)
|
|
self.assertEqual(metric_container.metrics[0]._fn,
|
|
metrics_mod.sparse_categorical_accuracy)
|
|
|
|
metric_container = compile_utils.MetricsContainer('accuracy')
|
|
y_t, y_p = array_ops.ones((10, 20)), array_ops.ones((10, 20))
|
|
metric_container.update_state(y_t, y_p)
|
|
self.assertEqual(metric_container.metrics[0]._fn,
|
|
metrics_mod.categorical_accuracy)
|
|
|
|
def test_metric_weighting(self):
|
|
metric_container = compile_utils.MetricsContainer(
|
|
metrics=['mae'], weighted_metrics=['mae'])
|
|
|
|
y_t = ops.convert_to_tensor_v2_with_dispatch([[0], [3], [0]])
|
|
y_p = ops.convert_to_tensor_v2_with_dispatch([[0], [0], [0]])
|
|
sw = ops.convert_to_tensor_v2_with_dispatch([[1], [0], [1]])
|
|
|
|
metric_container.update_state(y_t, y_p, sample_weight=sw)
|
|
self.assertLen(metric_container.metrics, 2)
|
|
|
|
mae_metric = metric_container.metrics[0]
|
|
self.assertEqual(mae_metric.name, 'mae')
|
|
self.assertEqual(mae_metric.result().numpy(), 1.)
|
|
|
|
weighted_mae_metric = metric_container.metrics[1]
|
|
self.assertEqual(weighted_mae_metric.name, 'weighted_mae')
|
|
self.assertEqual(weighted_mae_metric.result().numpy(), 0.)
|
|
|
|
def test_broadcast_metrics_to_dict(self):
|
|
metric_container = compile_utils.MetricsContainer(metrics=['mae'])
|
|
|
|
y_p = {'output': ops.convert_to_tensor_v2_with_dispatch([[0], [1], [2]])}
|
|
y_t = {'output': ops.convert_to_tensor_v2_with_dispatch([[1], [2], [3]])}
|
|
metric_container.update_state(y_t, y_p)
|
|
|
|
mae_metric = metric_container.metrics[0]
|
|
self.assertEqual(mae_metric.name, 'mae')
|
|
self.assertEqual(mae_metric.result().numpy(), 1.)
|
|
|
|
def test_broadcast_metrics_to_dict_with_output_names(self):
|
|
metric_container = compile_utils.MetricsContainer(
|
|
metrics=['mae'], output_names=['output'])
|
|
|
|
y_p = ops.convert_to_tensor_v2_with_dispatch([[0], [1], [2]])
|
|
y_t = {'output': ops.convert_to_tensor_v2_with_dispatch([[1], [2], [3]])}
|
|
metric_container.update_state(y_t, y_p)
|
|
|
|
mae_metric = metric_container.metrics[0]
|
|
self.assertEqual(mae_metric.name, 'mae')
|
|
self.assertEqual(mae_metric.result().numpy(), 1.)
|
|
|
|
def test_missing_label_with_no_metrics(self):
|
|
# It's ok to exclude a label if that label has no
|
|
# losses or metrics associated with it.
|
|
metric_container = compile_utils.MetricsContainer(metrics={
|
|
'output1': 'mae',
|
|
'output3': 'mse'
|
|
})
|
|
|
|
y_p = {
|
|
'output1': ops.convert_to_tensor_v2_with_dispatch([[0], [1], [2]]),
|
|
'output2': ops.convert_to_tensor_v2_with_dispatch([[3], [4], [5]]),
|
|
'output3': ops.convert_to_tensor_v2_with_dispatch([[6], [7], [8]])
|
|
}
|
|
y_t = {
|
|
'output1': ops.convert_to_tensor_v2_with_dispatch([[1], [2], [3]]),
|
|
'output3': ops.convert_to_tensor_v2_with_dispatch([[4], [5], [6]])
|
|
}
|
|
|
|
metric_container.update_state(y_t, y_p)
|
|
self.assertLen(metric_container.metrics, 2)
|
|
|
|
mae_metric = metric_container.metrics[0]
|
|
self.assertEqual(mae_metric.name, 'output1_mae')
|
|
self.assertEqual(mae_metric.result().numpy(), 1.)
|
|
|
|
mse_metric = metric_container.metrics[1]
|
|
self.assertEqual(mse_metric.name, 'output3_mse')
|
|
self.assertEqual(mse_metric.result().numpy(), 4.)
|
|
|
|
def test_metrics_masking(self):
|
|
metrics_container = compile_utils.MetricsContainer(
|
|
metrics=['mae'], weighted_metrics=['mse'])
|
|
y_p = constant_op.constant([[[1], [1]], [[0], [0]]], dtype=dtypes.float32)
|
|
y_t = constant_op.constant([[[1], [1]], [[1], [1]]], dtype=dtypes.float32)
|
|
y_p._keras_mask = constant_op.constant([[1, 1], [0, 0]],
|
|
dtype=dtypes.float32)
|
|
|
|
metrics_container.update_state(y_t, y_p)
|
|
self.assertLen(metrics_container.metrics, 2)
|
|
|
|
mae_metric = metrics_container.metrics[0]
|
|
self.assertEqual(mae_metric.name, 'mae')
|
|
self.assertAlmostEqual(mae_metric.result().numpy(), 0)
|
|
|
|
weighted_mae_metric = metrics_container.metrics[1]
|
|
self.assertEqual(weighted_mae_metric.name, 'mse')
|
|
self.assertAlmostEqual(weighted_mae_metric.result().numpy(), 0)
|
|
|
|
def test_metrics_sample_weight(self):
|
|
metrics_container = compile_utils.MetricsContainer(
|
|
metrics=['mae'], weighted_metrics=['mse'])
|
|
y_p = constant_op.constant([[[1], [1]], [[0], [1]]], dtype=dtypes.float32)
|
|
y_t = constant_op.constant([[[1], [1]], [[1], [1]]], dtype=dtypes.float32)
|
|
sw = constant_op.constant([[.2, .3], [.5, 0]], dtype=dtypes.float32)
|
|
|
|
metrics_container.update_state(y_t, y_p, sample_weight=sw)
|
|
self.assertLen(metrics_container.metrics, 2)
|
|
|
|
mae_metric = metrics_container.metrics[0]
|
|
self.assertEqual(mae_metric.name, 'mae')
|
|
self.assertAlmostEqual(mae_metric.result().numpy(), .25) # 1 / 4
|
|
|
|
weighted_mae_metric = metrics_container.metrics[1]
|
|
self.assertEqual(weighted_mae_metric.name, 'mse')
|
|
self.assertAlmostEqual(weighted_mae_metric.result().numpy(), .5) # .5 / 1
|
|
|
|
def test_metrics_masking_sample_weight(self):
|
|
metrics_container = compile_utils.MetricsContainer(
|
|
metrics=['mae'], weighted_metrics=['mse'])
|
|
y_p = constant_op.constant([[[1], [1]], [[0], [1]]], dtype=dtypes.float32)
|
|
y_t = constant_op.constant([[[1], [1]], [[1], [1]]], dtype=dtypes.float32)
|
|
sw = constant_op.constant([[.3, .2], [.2, .3]], dtype=dtypes.float32)
|
|
y_p._keras_mask = constant_op.constant([[1, 0], [1, 0]],
|
|
dtype=dtypes.float32)
|
|
|
|
metrics_container.update_state(y_t, y_p, sample_weight=sw)
|
|
self.assertLen(metrics_container.metrics, 2)
|
|
|
|
mae_metric = metrics_container.metrics[0]
|
|
self.assertEqual(mae_metric.name, 'mae')
|
|
self.assertAlmostEqual(mae_metric.result().numpy(), .5) # 1 / .5
|
|
|
|
weighted_mae_metric = metrics_container.metrics[1]
|
|
self.assertEqual(weighted_mae_metric.name, 'mse')
|
|
self.assertAlmostEqual(weighted_mae_metric.result().numpy(), .2 / .5)
|
|
|
|
def test_loss_class_as_metric_with_distribution(self):
|
|
distribution = one_device_strategy.OneDeviceStrategy('/device:CPU:0')
|
|
with distribution.scope():
|
|
metric_container = compile_utils.MetricsContainer(
|
|
losses_mod.MeanSquaredError())
|
|
y_t, y_p = array_ops.ones((10, 5)), array_ops.zeros((10, 5))
|
|
metric_container.update_state(y_t, y_p)
|
|
|
|
self.assertLen(metric_container.metrics, 1)
|
|
metric = metric_container.metrics[0]
|
|
self.assertEqual(metric.name, 'mean_squared_error')
|
|
self.assertEqual(metric.result().numpy(), 1.)
|
|
|
|
def test_custom_metric_callables(self):
|
|
|
|
def custom_metric_fn(y_true, y_pred):
|
|
return math_ops.reduce_sum(y_true - y_pred)
|
|
|
|
class CustomMetricClass(object):
|
|
|
|
def __call__(self, y_true, y_pred):
|
|
return math_ops.reduce_sum(y_true - y_pred)
|
|
|
|
metric_container = compile_utils.MetricsContainer(
|
|
[custom_metric_fn, CustomMetricClass()])
|
|
y_t, y_p = array_ops.ones((10, 5)), array_ops.zeros((10, 5))
|
|
metric_container.update_state(y_t, y_p)
|
|
|
|
self.assertEqual(metric_container.metrics[0].name, 'custom_metric_fn')
|
|
self.assertEqual(metric_container.metrics[1].name, 'custom_metric_class')
|
|
|
|
|
|
if __name__ == '__main__':
|
|
ops.enable_eager_execution()
|
|
test.main()
|